Clinical Progression on CDR-SB©: Progression Free Time at Each 0.5-unit Level in Dominantly Inherited and Sporadic Alzheimer’s Disease Populations

Clinical Progression on CDR-SB ©: Progression Free Time at Each 0.5-unit Level in Dominantly Inherited and Sporadic Alzheimer’s Disease Populations | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Clinical Progression on CDR-SB © : Progression Free Time at Each 0.5-unit Level in Dominantly Inherited and Sporadic Alzheimer’s Disease Populations View ORCID Profile Guoqiao Wang , Yan Li , Eric McDade , Chengjie Xiong , Sarah M. Hartz , Randall J. Bateman , John C. Morris , Lon S. Schneider doi: https://doi.org/10.1101/2025.02.17.25322322 Guoqiao Wang 1 Department of Neurology, Washington University, School of Medicine , St. Louis, 63130, United States 2 Division of Biostatistics, Washington University School of Medicine in St. Louis , 660 S. Euclid Ave, St. Louis, MO 63110 Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Guoqiao Wang For correspondence: guoqiao{at}wustl.edu lschneid{at}usc.edu Yan Li 1 Department of Neurology, Washington University, School of Medicine , St. Louis, 63130, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site Eric McDade 1 Department of Neurology, Washington University, School of Medicine , St. Louis, 63130, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site Chengjie Xiong 2 Division of Biostatistics, Washington University School of Medicine in St. Louis , 660 S. Euclid Ave, St. Louis, MO 63110 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sarah M. Hartz 3 Department of Psychiatry, Washington University School of Medicine in St. Louis , 660 S. Euclid Ave, St. Louis, MO 63110 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Randall J. Bateman 1 Department of Neurology, Washington University, School of Medicine , St. Louis, 63130, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site John C. Morris 1 Department of Neurology, Washington University, School of Medicine , St. Louis, 63130, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lon S. Schneider 4 Department of Psychiatry and The Behavioral Sciences, Department of Neurology, Keck School of Medicine, University of Southern California , Los Angeles, 90033, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Data/Code Preview PDF ABSTRACT INTRODUCTION CDR-SB is a reliable and clinically meaningful composite for assessing treatment effects in Alzheimer’s disease (AD) clinical trials. Small CDR-SB differences at the end of a trial often lead to controversy in deriving clinically meaningful interpretations. METHODS We estimated progression-free time participants remained at each 0.5-unit CDR-SB increment in dominantly inherited AD (DIAD) and sporadic AD populations, evaluating its potential as an alternative measure of treatment effects. RESULTS Progression-free time is longer at CDR-SB ≤ 2.0 (1–2 years) and shorter at CDR-SB ≥ 5 (0.33 or less) in the ADNI cohort. The DIAD cohort showed similar but shorter times. Using progression-free time, continuous lecanemab treatment for three years is estimated to delay disease progression by 0.7 years in the sporadic population. DISCUSSION Progression-free time provides a benchmark for expressing clinical progression and treatment effects and can be applied particularly during open-label extensions and singlearm trials without placebo comparisons. 1 Background The Clinical Dementia Rating Scale - Sum of Boxes (CDR-SB © ) is widely used as a primary or secondary endpoint in Phase II and III clinical trials for Alzheimer’s disease (AD). 1 - 5 It serves as an FDA-approved composite measure for assessing mild cognitive impairment (MCI) and mild dementia, simplifying trials that might otherwise require multiple or co-primary endpoints. 6 In the CDR-SB assessment, a rater uses semi-structured interviews of the participant and an informant to assess the former’s performance across six domains: three cognitive (memory, orientation, judgment/problem-solving) and three functional (community affairs, home/hobbies, and personal care). Each domain is scored on an ordinal scale (0, 0.5, 1, 2, 3), indicating ‘no dementia,’ ‘uncertain,’ ‘mild,’ ‘moderate,’ and ‘severe’ dementia respectively, with the exception of personal care, which omits the 0.5 score. The scores across these domains are summed to yield the CDR-SB score which has a range of 0 to 18 with 0.5-point intervals at lower summary scores (i.e., better function). CDR-SB scores and the differences in change from baseline between treatment and placebo groups are prominently presented in the clinical studies sections of the prescribing information (i.e., “the label”) for the FDA approved amyloid-targeting antibodies, aducanumab, 1 lecanemab, 3 and donanemab, 4 providing patients and prescribers with insights into treatment outcomes. Although treatment-placebo differences of the CDR-SB are statistically significant for these antibodies and supported FDA’s approval of these antibodies for marketing, the actual mean effect sizes are modest, ranging from +0.03, 1 to -0.39, 1 -0.45, 3 and -0.70, 4 sparking considerable debate regarding their clinical interpretability and significance. Understanding how these numerical differences might translate into meaningful clinical outcomes is essential for clinicians, patients, and researchers alike. An Alzheimer’s Association workgroup suggested that expectations of outcomes from therapeutic interventions in AD may need to be modified. 7 One approach is to express differences in CDR-SB changes as a percentage reduction compared to the placebo comparator, e.g. x% slowing of progression. 2 - 4 Another is to interpret these differences as a delay in disease progression, either relative to the decline observed in the placebo group (e.g., 5.3 months for lecanemab) 4 , 8 or relative to reaching a specific disease stage milestone. 9 However, these methods are relative measures, vary with the change in placebo, and may not be applicable during the open-label extension (OLE) period when all participants receive treatment. Furthermore, these approaches are trial-specific and may lack comparability across trials due to differences in baseline CDR-SB levels and change in the placebo control groups. In this study, we introduce an alternative methodology that standardizes the evaluation of delays in disease progression based on the CDR scale. We accomplish this by calculating the progression-free time (or ‘residence time’) a research participant remains at a given CDR-SB level before progressing to a higher level. Specifically, we propose estimating the progression-free time at each 0.5-unit CDR-SB increment for two distinct populations: participants with dominantly inherited Alzheimer’s disease (DIAD) from the Dominantly Inherited Alzheimer Network (DIAN) observational study and those with sporadic Alzheimer’s disease from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study. We suggest using progression-free time, or time-to-0.5-unit progression, as a potential measure of clinically meaningful change. 2 Methods 2.1 Study Participants The Dominantly Inherited Alzheimer Network (DIAN) is an international, multicenter observational study focused on individuals aged 18 years or older who have parents with a known causative mutation in the amyloid precursor protein (APP), presenilin 1 (PSEN1) , or presenilin 2 (PSEN2) genes, for AD. Mutation carriers (MCs) face an almost certain risk of developing AD, with relatively predictable symptom onset ages. At enrollment into the DIAN cohort, these participants were either cognitively unimpaired (Clinical Dementia Rating [CDR] = 0) or showed signs of early symptomatic disease (CDR = 0.5 or 1, signifying very mild or mild dementia, respectively). Research participants with the Dutch mutation were excluded in this study due to their atypical rates of clinical progression. Detailed information on the DIAN study’s structure and the assessments performed can be found in previous publications. 10 , 11 The Alzheimer’s Disease Neuroimaging Initiative (ADNI) is a longitudinal, multicenter study designed to track the progression of Alzheimer’s disease using various biomarkers in individuals aged 55 to 90 years. ADNI encompasses participants across the spectrum of AD pathology, categorizing them into three groups at enrollment into ADNI: normal cognition, mild cognitive impairment (MCI), and AD dementia. Data included in this study were download from adni.loni.ucla.edu. 2.2 Statistical Analysis To map the holistic trajectory of disease progression from CDR-SB score of 0 (the lower bound) to a CDR-SB score of 18 (the upper bound), we applied a parametric disease progression model incorporating random effects for both the endpoint and the time variable (see Supplemental Material for details). In alignment with previous studies, 12 - 14 integrating a random effect into the time variable enables the model to accurately recover the underlying disease progression pathway by adjusting the time variable: shifting it leftward for less severe stages and rightward for more severe stages of the disease. For mutation carriers in the DIAN study, the Estimated Years from Symptom Onset (EYO) serves as a relatively precise indicator of their disease stage, thus, EYO was adopted as the time variable in our model. 12 Conversely, for participants in the ADNI study, lacking such an accurate measure, the time since enrollment in years was used as the time variable. 13 , 14 Following the estimation of the disease progression trajectory, the progression-free time—the duration spent at each 0.5-unit interval of CDR-SB progression— was calculated based on the rate of disease progression observed in each interval. To demonstrate the potential use of progression-free time, we assessed the treatment effect of lecanemab during the OLE period as a time delay in disease progression. 15 This was followed by a comparison with the delay in disease progression as estimated by conventional methods, such as calculating the time based on the percentage reduction in disease progression compared to placebo, multiplied by the total duration. 8 All statistical analyses were conducted using SAS software, version 9.4. We utilized two-sided t-tests with a Type I error rate of 5% and reported the corresponding 95% confidence intervals (CIs). 3 Results 3.1 Progression-free Time for DIAD and Sporadic AD Populations In the DIAN study, 384 mutation carriers were included from data freeze 17 (without Dutch mutations), while in the ADNI study, 406 participants were involved. The baseline characteristics of these participants are detailed in Table 1 . View this table: View inline View popup Download powerpoint Table 1: Baseline characteristics for each study Figure 1 illustrates the progression-free time at each 0.5-unit increment of the CDR-SB score, up to 12.5, for the DIAD population. The progression-free time at CDR-SB 0.5 is approximately 1.65 years, which sharply declines to less than 0.5 years once the CDR-SB score reaches 2.5 or above Supplemental Table 1 presents the progression-free time for all intervals at 0.5-unit increments of the CDR-SB score. Download figure Open in new tab Figure 1: Estimated progression-free time and its 95% CI for each 0.5-unit CDR-SB interval based on the DIAN study Figure 2 presents similar data for the sporadic AD population, showing progression-free times for each 0.5-unit increment of the CDR-SB score, also up to 12.5. Here, the progression-free time from CDR-SB 0.5 to CDR-SB 1 is roughly 2.25 years, which then rapidly decreases to under 0.5 years when the CDR-SB score surpasses 3.5. The progression-free time for all intervals at 0.5-unit increments of the CDR-SB score are presented in Supplemental Table 1. Download figure Open in new tab Figure 2: Estimated progression-free time and its 95% CI for each 0.5-unit CDR-SB interval based on the ADNI study 3.2 Application of Progression-free Time to Assess Treatment Effects According to reports from the OLE period of the phase III lecanemab trial, participants in the early-start group, who began treatment from the double-blind baseline, showed a decline of 3.09 units in the CDR-SB score over 3 years, moving from a baseline of 3.17 to 6.26. 3 , 15 Since participants in the original placebo group transitioned to lecanemab treatment during the OLE (becoming the delayed-start group), it’s not feasible to directly assess the treatment’s effect in the early-start group by comparing it with the delayed-start group during this period. Instead, we could evaluate the treatment effect using the estimated progression-free time ( Figure 2 ) for the sporadic AD population. Our estimation suggests that without lecanemab treatment, participants might have progressed from a CDR-SB score of 3.17 to 6.26 in just 2.3 years. However, with continuous lecanemab treatment, this progression was extended to 3 years, effectively a time difference or delay of progression of 0.7 years. As an alternative approach to assess the treatment effect, a cohort from the ADNI study, matched as external controls, was used to benchmark disease progression from the double-blind baseline to the 36-month mark, including 18 months within the OLE period. 15 It was observed that the matched ADNI controls experienced a decline of 4.14 units in the CDR-SB score, which was 0.95 units greater than the decline in the early-start group receiving lecanemab. 15 To quantify the delay in disease progression using the percentage reduction method, 5 we first calculated the percentage reduction in disease progression relative to the ADNI group. We then multiplied this value by the total study duration in years. This calculation yielded a delay of 0.7 years, derived from the formula (0.95/4.14) x (36/12), 5 which aligns well with the findings obtained using the progression-free time approach. 4 Discussion CDR-SB has been demonstrated as a reliable composite measure for assessing treatment effects in Alzheimer’s disease clinical trials and its use in AD drug development is ongoing. 1 , 3 , 4 Consequently, there is a great interest among clinicians, patients, and researchers to derive meaningful interpretations from the treatment effects measured by the change over time and treatment-control differences in CDR-SB in trials. 7 Various approaches have been suggested to quantify these effects, including: (i) Expressing the difference in CDR-SB scores between treatment and control groups as mean differences; (ii) in terms of percentage reduction compared to controls ; 3 , 4 (iii) translating these differences into time savings relative to the decline observed in the control or placebo group; 4 , 8 , 16 (iv) calibrating the treatment effect to reflect delays in reaching critical milestones, such as the loss of independence in activities of daily living. 9 These methodologies have significantly enhanced our ability to interpret the outcomes of recent amyloid-targeting antibody trials. 3 , 4 Building on these, our study introduces an alternative approach by calculating the progression-free time for each 0.5-unit increment in the CDR-SB score for both DIAD and sporadic AD populations. Our findings indicate that the DIAD population progresses faster than the sporadic AD population, consistent with previous reports based on cognitive endpoints. 17 Applying this progression-free-time approach to quantify treatment effects in the Clarity AD trial during its 18-month OLE period yielded an estimated 0.7-year delay in disease progression, aligning closely with results from the existing method proposed by Wang. et al. 5 This suggests that progression-free time, or ‘residence time,’ at a given 0.5-unit increment in CDR-SB can provide a standardized measure of treatment effects. The concept also offers clinical relevance: during early AD, patients with lower CDR-SB scores (e.g., 0.5–1.0) may remain stable over 1–2 years, making even a 0.5-point decrease clinically significant. Conversely, at higher CDR-SB scores (e.g., ≥2.5), progression-free time shortens to approximately 0.5–0.6 years, where a 1.0-point reduction may represent a minimal clinically important difference. By quantifying the time patients remain at specific disease stages, progression-free time provides a useful tool for clinical interpretation of treatment effects over extended periods, particularly during OLE periods, and in single-arm trials where placebo comparisons are unavailable. Our study represents an initial attempt to quantify progression-free time for each CDR-SB 0.5 unit; however, several limitations should be considered. First, our estimates are based on a parametric model, which, although fitting the data reasonably well, requires further validation to assess its robustness. Future studies should explore alternative models such as time-to-event based models to confirm the consistency of our findings. Second, our results need validation using different study cohorts to assess variability. Finally, we did not report progression-free time from CDR-SB 0 to CDR-SB 0.5 because an individual’s CDR-SB 0 stage can last for many years, and the estimated transition time depends on when participants enter the study. Future research could explore progression-free time from an earlier benchmark, such as amyloid positivity in cognitively normal individuals, to CDR-SB 0.5, providing a clearer timeline of disease progression during this stage. Much like oncology’s use of progression-free survival, a progression-free time chart could serve as a benchmark for understanding AD progression. This approach would allow clinicians to communicate the rate of cognitive decline in a more accessible and intuitive manner, facilitating clearer discussions with patients and caregivers. By framing disease progression in terms of the time delayed in disease progression by stage of illness, or the potential extension of stability offered by treatment, this method translates abstract concepts into tangible metrics that may be more interpretable by patients, families and physicians. For patients, families, and clinicians such a chart could simplify the understanding of disease progression and treatment impacts, enhancing engagement and adherence to treatment protocols. More informed discussions about AD treatment effects may be provided by integrating progression-free time metrics into clinical practice, helping set realistic expectations and plan for future care needs. Data Availability All data produced are available online at: https://adni.loni.usc.edu/data-samples/adni-data/ https://dian.wustl.edu/observational-study/ https://adni.loni.usc.edu/data-samples/adni-data/ Supplemental Materials Parametric disease progression model (DPM) Briefly, the model applied to the DIAN study can be described as: Where, Y ij denotes the assessment for subject i at visit j ( EYO ij ) β is the mean of the healthy period (e.g. EYO <=-15) γ 1 is the shape parameter and influences how much steep the curve exhibits. It also represents the mean decline from the healthy period to symptom onset (e.g. EYO=0) γ 2 represents the scale parameter, and affects the rate at which the exponential function grows or decays μ i is the subject-level random effect that shifts the individual data up or down on the y-axis, accounting for individual variability in the baseline or intercept of the response variable δ i is the subject-level random effect that shifts the individual data left or right on the x-axis, adjusting for individual variability in the timing or progression from symptom onset μ i and δ i are assumed to follow a bivariate normal distribution: ε ij is the within-subject error and is assumed to follow a normal distribution for participants with CDR 0 at baseline and for participants with CDR>0. For ADNI study, the same model is applied with EYO being replaced by the time since enrollment in years. After the DPM is estimated, the progression time to each CDR-SB level was calculated based on the model. Then the progression free time was calculated as the difference between the progression time corresponding to two consecutive CDR-SB levels. View this table: View inline View popup Supplemental Table 1: Estimated progression-free time for each 0.5-unit CDR-SB interval for each study Disclosure of Conflict of Interests Guoqiao Wang, PhD, is the biostatistics core co-leader for the DIAN-TU. He reports serving on a Data Safety Committee for Amydis Corporate, Abata Therapeutics, and statistical consultant for Eisai inc. and Alector Inc. Eric McDade, D.O., is the Associate Director of the DIAN-TU. He reports serving on a Data Safety Committee for Eli Lilly and Company and Alector; scientific consultant for Eisai and Eli Lilly and Company; institutional grant support from Eli Lilly and Company, F. Hoffmann-La Roche, Ltd. and Janssen. Chengjie Xiong, PhD, reports serving as a statistical consultant for Diadem and for FDA Advisory Committee on Imaging Medical Products. He also receives support from the following grants: NIHGrantAG067505. Randall J. Bateman, M.D., is the Director of the DIAN-TU and Principal Investigator of the DIAN-TU-001. He co-founded C2N Diagnostics. Washington University and R.J.B. have equity ownership interest in C2N Diagnostics and receive royalty income based on technology (stable isotope labeling kinetics, blood plasma assay and methods of diagnosing Alzheimer’s disease with phosphorylation changes) that is licensed by Washington University to C2N Diagnostics. R.J.B. receives income from C2N Diagnostics for serving on the scientific advisory board. R.J.B. has received research funding from Avid Radiopharmaceuticals, Janssen, Roche/Genentech, Eli Lilly, Eisai, Biogen, AbbVie, Bristol Myers Squibb and Novartis. He receives research support from the National Institute on Aging of the National Institutes of Health, DIAN-TU Trial Pharmaceutical Partners (Eli Lilly and Company, F. Hoffman-La Roche, Ltd., and Avid Radiopharmaceuticals), Alzheimer’s Association, GHR Foundation, Anonymous Organization, DIAN-TU Pharma Consortium (Active: Biogen, Eisai, Eli Lilly and Company, Janssen, F. Hoffmann-La Roche, Ltd./Genentech. Previous: AbbVie, Amgen, AstraZeneca, Forum, Mithridion, Novartis, Pfizer, Sanofi, United Neuroscience). He has been an invited speaker for Novartis and serves on the Advisory Board for F. Hoffman La Roche, Ltd. Dr. John Morris reports serving as a consultant for Barcelona Brain Research Center BBRC and Native Alzheimer Disease-Related Resource Center in Minority Aging Research, Ext Adv Board, serving on a Data Safety Committee for Cure Alzheimer’s Fund, Research Strategy Council and LEADS Advisory Board, Indiana University. Lon Schneider, M.D., reports within a 36 month time frame, grants from NIH R01 AG054434 (Delivery of Essential Fatty Acids to the Brain in Alzheimer’s disease), P30 AG066530 (USC ADRC), R01 AG062687, R01 AG051346, R01 AG055444, P01 AG02350, R01 AG053267 (DIAN-TU), R01 AG074983 (Aβ and tau vaccines), R01 AG063826, State of California CADC 15-10291; grants and personal fees from Eli Lilly/Avid, grants and personal fees from Roche/Genentech; grants/contracts from Eisai, Biogen, and Biohaven; grants from Washington University, St. Louis/ NIA DIAN-TU, grants from UC San Diego ADCS, personal fees from Neurim, Ltd (Israel), Cognition Therapeutics, Corium, Immunobrain Checkpoint, Ltd (Israel), Alpha-cognition, BioVie, Lexeo, Lighthouse, AC Immune (Suisse), Athira, GW Research (UK, Jazz, USA), Merck, Otsuka (USA), Pharmatrophix, Linus Health, Lundbeck, Novo Nordisk, Muna (Denmark), Ono Ltd (Japan), Vivli.org, outside the submitted work; and from The Della Martin Foundation endowment. All the other authors reported no conflicts of interest. Funding Sources Research reported in this publication was supported by the National Institute on Aging of the National Institutes of Health under Award Numbers U01AG042791, U01AG042791-S1 (FNIH and Accelerating Medicines Partnership), R01AG046179, R01AG053267, R01AG053267-S1, R01AG068319, and 1U01AG059798, P30AG066530 (USC ADRC). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Data collection and sharing for this project was supported by The Dominantly Inherited Alzheimer Network (DIAN, U19AG032438) funded by the National Institute on Aging (NIA), the Alzheimer’s Association (SG-20-690363-DIAN), the German Center for Neurodegenerative Diseases (DZNE), Raul Carrea Institute for Neurological Research (FLENI), Partial support by the Research and Development Grants for Dementia from Japan Agency for Medical Research and Development (AMED), the Korea Health Technology R×D Project through the Korea Health Industry Development Institute (KHIDI), Korea Dementia Research Center (KDRC), funded by the Ministry of Health × Welfare and Ministry of Science and ICT, Republic of Korea (HI21C0066), and Spanish Institute of Health Carlos III (ISCIII). This manuscript has been reviewed by DIAN Study investigators for scientific content and consistency of data interpretation with previous DIAN Study publications. We acknowledge the altruism of the participants and their families and contributions of the DIAN research and support staff at each of the participating sites for their contributions to this study Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc .; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc .; Cogstate; Eisai Inc .; Elan Pharmaceuticals, Inc .; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc .; Fujirebio; GE Healthcare; IXICO Ltd .; Janssen Alzheimer Immunotherapy Research & Development, LLC .; Johnson & Johnson Pharmaceutical Research & Development LLC .; Lumosity; Lundbeck; Merck & Co ., Inc .; Meso Scale Diagnostics, LLC .; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc .; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( www . fnih . org ). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. Diffusion MRI data used in the preparation of this article were obtained from the Lifespan human connectome project in aging. The HCP Lifespan project was supported by the National Institute on Aging of the National Institutes of Health under Award Number U01AG052564 and by funds provided by the McDonnell Center for Systems Neuroscience at Washington University in St. Louis. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health . ” DIAN Observational Study Site Investigators James Noble, MD, Columbia University, New York Martin Farlow, MD, Indiana University, Indianapolis, IN Jasmeer Chhatwal, MD, PhD, Brigham and Women’s Hospital-Massachusetts GH, Charlestown, MA Stephen Salloway, MD, Butler Hospital, Warren Alpert School of Medicine, Brown University Sarah Berman, MD, PhD, University of Pittsburgh, PA Gregg Day, MD, Mayo Clinic Jacksonville, FL Hiroyuki Shimada, MD, PhD, Osaka City University, Japan Takeshi Ikeuchi, MD, PhD, Brain Research Institute, Nigata, Japan Kazushi Suzuki, MD, PhD, The University of Tokyo, Japan Peter Schofield, PhD, DSc, Neuroscience Research Australia, Sydney Ralph Martins, BSc, PhD, Edith Cowan University, Nedlands, Western Australia Nick Fox, MD, FRCP, FMedSci, Dementia Research Centre, University College London, United Kingdom Johannes Levin, MD, PhD, German Center for Neurodegenerative Diseases (DZNE), Munich, Germany Mathias Jucker, PhD, German Center for Neurodegenerative Diseases (DZNE), Tubingen, Germany Raquel Sanchez Valle, MD, Hospital Clinic i Provincial de Barcelona, Spain Patricio Chrem, MD, Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia (FLENI), Buenos Aires, Argentina DIAN EXR Referring Clinicians, Researchers and Partner Sites Neelum T. Aggarwal, MD, Rush University Medical Center, Chicago IL Tom Ala, Center for Alzheimer’s Disease and Related Disorders, Southern Illinois University School of Medicine Thomas Bird, University of Washington, Seattle Sandra E. Black, Sunnybrook Health Sciences Centre, University of Toronto, Canada William J. Burke, MD, Banner Alzheimer’s Institute Cynthia M. Carlsson, MD, MS, University of Wisconsin School of Medicine and Public Health Andrew Frank M.D. B.Sc.H. F.R.C.P.(C), Bruyere Continuing Care, Ottawa, Ontario, Canada James E. Galvin, MD, MPH, Charles E. Schmidt College of Medicine, Florida Atlantic University Alvin C Holm, MD, Bethesda Hospital, St. Paul, MN John S.K. Kauwe, Brigham Young University David Knopman MD, Mayo Clinic, Rochester MN Sarah Kremen, MD, University of California, Los Angeles Alan J. Lerner, University Hospitals Cleveland Medical Center Barry S. Oken, MD, PhD, Oregon Health & Science University Hamid R. Okhravi, Eastern Virginia Medical School Ronald C. Petersen, Mayo Clinic, Rochester, MN Aimee L. Pierce, MD, University of California Irvine Marsha J. Polk, MED, University of Texas Health Science Center at San Antonio John M. Ringman, MD, MS, University Southern California Peter St. George Hyslop, MD, FRS, FRSC, FRCPC, University of Toronto Sanjeev N. Vaishnavi, MD, PhD, University of Pennsylvania Sandra Weintraub, Northwestern University Feinberg School of Medicine, IL 5 Reference 1. ↵ Budd Haeberlein S , Aisen P , Barkhof F , et al. Two randomized phase 3 studies of aducanumab in early Alzheimer’s disease . The Journal of Prevention of Alzheimer’s Disease . 2022 ; 9 ( 2 ): 197 – 210 . OpenUrl 2. ↵ Salloway S , Farlow M , McDade E , et al. A trial of gantenerumab or solanezumab in dominantly inherited Alzheimer’s disease . Nature Medicine . 2021 : 1 – 10 . 3. ↵ Van Dyck CH , Swanson CJ , Aisen P , et al. Lecanemab in early Alzheimer’s disease . New England Journal of Medicine . 2023 ; 388 ( 1 ): 9 – 21 . 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Statistical disease progression modeling in Alzheimer disease . Frontiers in big Data . 2020 ; 3 : 24 . OpenUrl PubMed 14. ↵ Donohue MC , Jacqmin-Gadda H , Le Goff M , et al. Estimating long-term multivariate progression from short-term data . Alzheimer’s & Dementia . 2014 ; 10 : S400 – S410 . OpenUrl 15. ↵ Sperling RA , van Dyck CH . Does the Current Evidence Base Support Lecanemab Continued Dosing for Early Alzheimer’s Disease? Paper presented at: Alzheimer’s Association International Conference 2024 . 16. ↵ Dickson S , Wessels AM , Dowsett S , et al. ‘Time saved’as a demonstration of clinical meaningfulness and illustrated using the donanemab TRAILBLAZER-ALZ study findings . The Journal of Prevention of Alzheimer’s Disease . 2023 : 1 – 5 . 17. ↵ Xiong C , McCue LM , Buckles V , et al. Cross-sectional and longitudinal comparisons of biomarkers and cognition among asymptomatic middle-aged individuals with a parental history of either autosomal dominant or late - onset Alzheimer’s disease . Alzheimer’s & Dementia . 2023 ; 19 ( 7 ): 2923 – 2932 . OpenUrl View the discussion thread. Back to top Previous Next Posted February 20, 2025. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Clinical Progression on CDR-SB ©: Progression Free Time at Each 0.5-unit Level in Dominantly Inherited and Sporadic Alzheimer’s Disease Populations Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. 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